Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion

2026-08-27

AI Summary

本文提出了一种条件扩散模型，直接从原始SIDIS事件动量学中学习TMD PDFs，解决了传统方法依赖参数化函数形式和迭代拟合的局限性。

Abstract

Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a central goal of the nucleon structure program at Jefferson Lab and the future Electron-Ion Collider. Traditional extraction methods rely on parameterized functional forms and iterative fitting, which can limit the flexibility of the resulting distributions and make uncertainty quantification cumbersome. We present a conditional diffusion model that learns to map raw SIDIS event kinematics directly to TMD PDFs, bypassing explicit functional assumptions. Evaluated on simulated SIDIS data at CLAS12 kinematics, the model recovers the underlying TMD with informative uncertainties that narrow steadily with increasing event statistics, and produces reliable estimates even with as few as 1,000 conditioning events, a statistics-limited regime directly relevant to ongoing and planned experiments.

Problem

• Transverse Momentum Dependent Parton Distribution Functions
• Semi-Inclusive Deep Inelastic Scattering
• Uncertainty Quantification

Innovation

• Conditional Diffusion Model
• Raw SIDIS Event Kinematics
• TMD PDFs
• Uncertainty Quantification